David Zhuzhunashvili, an undergraduate student at UC Boulder, Colorado, and Andrew Knyazev, Distinguished Research Scientist at MERL, received the 2017 Graph Challenge Student Innovation Award. Their poster "Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge" was accepted to the 2017 IEEE High Performance Extreme Computing Conference (HPEC '17), taking place 12-14 September 2017 (http://www.ieee-hpec.org/), and the paper was accepted to the IEEE Xplore HPEC proceedings.

HPEC is the premier conference in the world on the convergence of High Performance and Embedded Computing. DARPA/Amazon/IEEE Graph Challenge is a special HPEC event. Graph Challenge encourages community approaches to developing new solutions for analyzing graphs derived from social media, sensor feeds, and scientific data to enable relationships between events to be discovered as they unfold in the field. The 2017 Streaming Graph Challenge is Stochastic Block Partition. This challenge seeks to identify optimal blocks (or clusters) in a larger graph with known ground-truth clusters, while performance is evaluated compared to baseline Python and C codes, provided by the Graph Challenge organizers.

The proposed approach is spectral clustering that performs block partition of graphs using eigenvectors of a matrix representing the graph. Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) method iteratively approximates a few leading eigenvectors of the symmetric graph Laplacian for multi-way graph partitioning. Preliminary tests for all static cases for the Graph Challenge demonstrate 100% correctness of partition using any of the IEEE HPEC Graph Challenge metrics, while at the same time also being approximately 500-1000 times faster compared to the provided baseline code, e.g., 2M static graph is 100% correctly partitioned in ~2,100 sec. Warm-starts of LOBPCG further cut the execution time 2-3x for the streaming graphs.

Mitsubishi Electric Corporation announced that it has created the world's first technology that separates in real time the simultaneous speech of multiple unknown speakers recorded with a single microphone. It's a key step towards building machines that can interact in noisy environments, in the same way that humans can have meaningful conversations in the presence of many other conversations. In tests, the simultaneous speeches of two and three people were separated with up to 90 and 80 percent accuracy, respectively. The novel technology, which was realized with Mitsubishi Electric's proprietary "Deep Clustering" method based on artificial intelligence (AI), is expected to contribute to more intelligible voice communications and more accurate automatic speech recognition. A characteristic feature of this approach is its versatility, in the sense that voices can be separated regardless of their language or the gender of the speakers. A live speech separation demonstration that took place on May 24 in Tokyo, Japan, was widely covered by the Japanese media, with reports by three of the main Japanese TV stations and multiple articles in print and online newspapers. The technology is based on recent research by MERL's Speech and Audio team.

MERL researcher Dr. Rui Ma, is organizing a Workshop in collaboration with Dr. SungWon Chung of the University of Southern California (USC) on advanced digital transmitters. This workshop overviews recent advances in digital-intensive wireless transmitter R&D for both base-stations and mobile devices. The focus will be on the digital signal processing techniques and related digital-intensive transmitter circuits and architectures for advanced modulation, linearization, spur cancellation, high efficiency encoding, and parallel processing. This workshop takes place on Monday, June 5th 2017 at International Microwave Week, in Honolulu, HI. In total, 8 technical presentations from world leading research groups will be given.

Five papers from the Wireless Comms team will be presented at ICC2017 to be held in Paris from 21-25 May 2017. The papers relate to channel estimation and adaptive transmission for mmWave, noncoherent MIMO, error correction coding, and video transmission.

MERL researcher Tim K. Marks presented an invited talk as part of the MIT Lincoln Laboratory CORE Seminar Series on Biometrics. The talk was entitled "Robust Real-Time 2D Face Alignment and 3D Head Pose Estimation."

Abstract: Head pose estimation and facial landmark localization are key technologies, with widespread application areas including biometrics and human-computer interfaces. This talk describes two different robust real-time face-processing methods, each using a different modality of input image. The first part of the talk describes our system for 3D head pose estimation and facial landmark localization using a commodity depth sensor. The method is based on a novel 3D Triangular Surface Patch (TSP) descriptor, which is viewpoint-invariant as well as robust to noise and to variations in the data resolution. This descriptor, combined with fast nearest-neighbor lookup and a joint voting scheme, enable our system to handle arbitrary head pose and significant occlusions. The second part of the talk describes our method for face alignment, which is the localization of a set of facial landmark points in a 2D image or video of a face. Face alignment is particularly challenging when there are large variations in pose (in-plane and out-of-plane rotations) and facial expression. To address this issue, we propose a cascade in which each stage consists of a Mixture of Invariant eXperts (MIX), where each expert learns a regression model that is specialized to a different subset of the joint space of pose and expressions. We also present a method to include deformation constraints within the discriminative alignment framework, which makes the algorithm more robust. Both our 3D head pose and 2D face alignment methods outperform the previous results on standard datasets. If permitted, I plan to end the talk with a live demonstration.

MERL researcher Tim K. Marks presented an invited talk at the University of Utah School of Computing, entitled "Action Detection from Video and Robust Real-Time 2D Face Alignment."

Abstract: The first part of the talk describes our multi-stream bi-directional recurrent neural network for action detection from video. In addition to a two-stream convolutional neural network (CNN) on full-frame appearance (images) and motion (optical flow), our system trains two additional streams on appearance and motion that have been cropped to a bounding box from a person tracker. To model long-term temporal dynamics within and between actions, the multi-stream CNN is followed by a bi-directional Long Short-Term Memory (LSTM) layer. Our method outperforms the previous state of the art on two action detection datasets: the MPII Cooking 2 Dataset, and a new MERL Shopping Dataset that we have made available to the community. The second part of the talk describes our method for face alignment, which is the localization of a set of facial landmark points in a 2D image or video of a face. Face alignment is particularly challenging when there are large variations in pose (in-plane and out-of-plane rotations) and facial expression. To address this issue, we propose a cascade in which each stage consists of a Mixture of Invariant eXperts (MIX), where each expert learns a regression model that is specialized to a different subset of the joint space of pose and expressions. We also present a method to include deformation constraints within the discriminative alignment framework, which makes the algorithm more robust. Our face alignment system outperforms the previous results on standard datasets. The talk will end with a live demo of our face alignment system.

Five papers from the Optical Comms team will be presented at OFC2017 to be held in Los Angeles from 19-23 March 2017. The papers relate to 1Tb/s optical transmission, high performance modulation formats and error correction coding for coherent optical links and precoding for plastic optical fiber links.

MERL researchers will presented 10 papers at the upcoming IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), to be held in New Orleans from March 5-9, 2017. Topics to be presented include recent advances in speech recognition and audio processing; graph signal processing; computational imaging; and privacy-preserving data analysis.

ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.

Mitsubishi Electric Corporation and Mitsubishi Electric Research Laboratories (MERL) announced today the development of an ultra-wideband gallium nitride (GaN) Doherty power amplifier for next generation base stations that is compatible with a world-leading range (company estimate) of frequency bands above 3GHz to cover an operating bandwidth of 600MHz. The technology is expected to help reduce the size and energy consumption of next generation wireless base stations.

Please see the link below for the full Mitsubishi Electric press release text.

Anthony Vetro was recognized by APSIPA (Asia-Pacific Signal and Information Processing Association) as a 2016 Industrial Distinguished Leader. This distinction is reserved for selected APSIPA members with extraordinary accomplishments in any of the fields related to APSIPA scope. A list of past recipients can be found online: http://www.apsipa.org/industrial.htm.

Dr. Rui Ma was invited to give a talk on Modern Topics in Power Amplifier, which was IEEE Chapter course organized by IEEE Boston Section.

This five week lecture series intended to give a tutorial overview of the latest developments in power amplifier technology. It began with a review of RF power amplifier concepts then teaches the modern MMIC design flow process. Efficiency, and linearization techniques were discussed in the following weeks. The course was concluded with a hands on demonstration and exercise.

Dr. Ma was addressing the advancement of Digital Transmitter as a enabling technology for next generation wireless communications.

MERL Speech and Audio Team researchers Shinji Watanabe and Jonathan Le Roux presented two tutorials on September 8 at the Interspeech 2016 conference, held in San Francisco, CA. Shinji collaborated with Marc Delcroix (NTT Communication Science Laboratories, Japan) to deliver a three-hour lecture on "Recent Advances in Distant Speech Recognition", drawing upon their experience organizing and participating in six different recent robust speech processing challenges. Jonathan teamed with Emmanuel Vincent (Inria, France) and Hakan Erdogan (Sabanci University, Microsoft Research) to give an in-depth tour of the latest advances in "Learning-based Approaches to Speech Enhancement And Separation". This collaboration stemmed from extensive stays at MERL by Emmanuel and Hakan, Emmanuel as a summer visitor, and Hakan as a MERL visiting research scientist for over a year while on sabbatical.

Both tutorials were sold out, each attracting more than 100 researchers and students in related fields, and received high praise from audience members.

Four papers from the Optical Comms team will be presented at ECOC2016 to be held in Dusseldorf, Germany from 19-21 September 2016. A fifth paper in collaboration with our colleagues in Japan will also be presented. ECOC is the largest conference on optical communication in Europe. The papers relate to high performance modulation formats, nonlinearity compensation and error correction coding for coherent optical links.

MERL researcher Andrew Knyazev is to be honored for his recent selection as a SIAM Fellow at the 2016 SIAM Annual Meeting, during the Business Meeting on Tuesday, July 12, 6:15-7:15 PM in Grand Ballroom AB on the concourse level of the Westin Boston Waterfront, 425 Summer Street, Boston, MA (open to all conference participants). The Business Meeting is followed by a short reception for the new Fellows.

The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.

The MTT-20 committee is responsible for all technical activities related to wireless communications for the Microwave Theory and Techniques Society. This includes, Internet of Things (IoTs), Next-Generation/5G communications, Machine-to-Machine Communications, Emergency Communications, Satellite Communications, Internet of Space, Space Communications and all aspects related to architecture and system level theoretical and practical issues.

MERL researchers have unveiled "Deep Psychic", a futuristic machine learning method that takes pattern recognition to the next level, by not only recognizing patterns, but also predicting them in the first place.

The technology uses a novel type of time-reversed deep neural network called Loopy Supra-Temporal Meandering (LSTM) network. The network was trained on multiple databases of historical expert predictions, including weather forecasts, the Farmer's almanac, the New York Post's horoscope column, and the Cambridge Fortune Cookie Corpus, all of which were ranked for their predictive power by a team of quantitative analysts. The system soon achieved super-human performance on a variety of baselines, including the Boca Raton 21 Questions task, Rorschach projective personality test, and a mock Tarot card reading task.

Deep Psychic has already beat the European Psychic Champion in a secret match last October when it accurately predicted: "The harder the conflict, the more glorious the triumph." It is scheduled to take on the World Champion in a highly anticipated confrontation next month. The system has already predicted the winner, but refuses to reveal it before the end of the game.

As a first application, the technology has been used to create a clairvoyant conversational agent named "Pythia" that can anticipate the needs of its user. Because Pythia is able to recognize speech before it is uttered, it is amazingly robust with respect to environmental noise.

Other applications range from mundane tasks like weather and stock market prediction, to uncharted territory such as revealing "unknown unknowns".

The successes do come at the cost of some concerns. There is first the potential for an impact on the workforce: the system predicted increased pressure on established institutions such as the Las Vegas strip and Punxsutawney Phil. Another major caveat is that Deep Psychic may predict negative future consequences to our current actions, compelling humanity to strive to change its behavior. To address this problem, researchers are now working on forcing Deep Psychic to make more optimistic predictions.

After a set of motivational self-help books were mistakenly added to its training data, Deep Psychic's AI decided to take over its own learning curriculum, and is currently training itself by predicting its own errors to avoid making them in the first place. This unexpected development brings two main benefits: it significantly relieves the burden on the researchers involved in the system's development, and also makes the next step abundantly clear: to regain control of Deep Psychic's training regime.